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检索条件"主题词=detection algorithms"

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detection Scheme for MIMO Systems Based on Covariance Shaping Least Squares Estimator in Quantum signal processing

Detection Scheme for MIMO Systems Based on Covariance Shapin...

引用

9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP'08)

作者： Sheng-mei ZHAO Zhi-gang SHEN Bao-yu ZHENG Nanjing University of Posts & Telecommunications NanjingChina210003

Covariance shaping least squares estimator is a new kind of linear estimator based on covariance shaping in quantum signal processing (QSP) *** this paper,we apply the covariance shaping least squares (CSLS) estimator to the detection of MIMO systems that leads to a new detection algorithm,covariance shaping MIMO detection *** derive the formalism of covariance shaping MIMO detection algorithm,and analyze its performance by numerical *** results show that the performance of system is better than that of detection algorithm with Zero-forcing and MMSE solutions,and the objective covariance matrix plays an important role in the proposed scheme

关键词： MIMO Least squares approximation Signal processing Signal processing algorithms Quantum mechanics detection algorithms Multiuser detection Communication systems Hilbert space Mechanical variables measurement

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An Algorithm for Spatial Outlier detection Based on Delaunay Triangulation

An Algorithm for Spatial Outlier Detection Based on Delaunay...

引用

International Conference on Computational Intelligence and Security

作者： Min-qi Zheng Chongcheng Chen Jia-xiang Lin Ming-hui Fan Tamás Janscó Spatial Information Research Center of Fujian Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Fuzhou China Spatial Information Research Center of Fujian Fuzhou University Key Lab of Data Mining & Information Sharing of Ministry of Education Fuzhou PRC Faculty of Geo-informatics University of West Hungary Szekesfehervar Hungary

Spatial outliers represent spatial objects whose non-spatial attributes are significantly different from their spatial neighborhoods. Spatial outlier detection can provide user with unexpected, interesting and useful spatial patterns for further analysis and has received a lot of attention. However, many existing methods for spatial outlier mining use the k-neighborhood method to determine spatial neighborhood which depends on a priori parameter k, and don¿t consider spatial autocorrelation. As a result, it usually violates the true situation. So we propose a similarity measurement between spatial objects that based on Delaunay triangulation (DT_SOF), which captures spatial correlation and spatial neighborhood from the dataset itself. Furthermore, the measure takes in account the local behavior of a spatial object in its neighborhood. Finally, experimentations on a synthetic and a real-world ecological geochemical dataset demonstrate that our approach can effectively detect spatial outliers with a lower disturbance by human intervention.

关键词： detection algorithms Iterative algorithms Data mining Humans Object detection Weather forecasting Data processing Testing Covariance matrix Computational intelligence

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A New Cloud detection Algorithm for FY-2C Images Over China

A New Cloud Detection Algorithm for FY-2C Images Over China

引用

International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Deren Li Xinyi Dong Liangming Liu Daxiang Xiang LIESMARS Wuhan University Wuhan Hubei China SRSAIE Wuhan University Wuhan Hubei China

FY-2C is a geostationary orbiting meteorological satellite designed by China independently. Based on statistical and spectral analysis of FY-2C data, this paper proposed a new cloud detection index operating accurately and quickly in spite of the low spatial resolution few spectral information. The diversity and sensitivity of cloud for each of the 4 bands of FY-2C data is also analyzed in this paper. The infrared red band 1, band 3 is found to be most suitable for cloud detection due to their spectral sensitivity of cloud.

关键词： Clouds detection algorithms Meteorology Satellite broadcasting Entropy Spatial resolution Data analysis Sea surface Ocean temperature Statistics

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A Novel Skin Tone detection Algorithm for Contraband Image Analysis

A Novel Skin Tone Detection Algorithm for Contraband Image A...

引用

International Workshop on Systematic Approaches to Digital Forensic Engineering (SADFE)

作者： Abhishek Choudhury Marcus Rogers Blair Gillam Keith Watson Purdue Cyber Forensics Lab USA Center for Education and Research in Information Assurance and Security Purdue Cyber Forensics Laboratory Purdue University

This paper examines skin tone detection algorithms used by first responder forensic tools such as File Hound. File Hound is a "field analysis" software application that is currently being used by over 100 law enforcement agencies, both internationally and domestically. It is mainly used in forensic investigations to search and identify pornographic images from a hard drive. Since the conception of File Hound, several steps have been taken to improve its performance and expand its features. One such feature is a skin tone detection filter that can identify images with a large skin color count from the aggregate image results found by File Hound. This filter is based on the idea that there is a positive correlation between images with a large skin color count and images that are pornographic in nature. A novel skin tone detection filter was developed and this filter was tested against random images obtained from the Compaq Image database for skin tone detection. The results of the test are encouraging in terms of accuracy and low error rates: type I = 20.64%, type II = 0.81%, accuracy = 78.55%.

关键词： Skin detection algorithms Image analysis Filters Forensics Testing Image color analysis Application software Law enforcement Aggregates

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detection of block-spread OFDM in fast fading

Detection of block-spread OFDM in fast fading

引用

Asia-Pacific Conference on Communications

作者： Michael McGuire Mihai Sima Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada

The block-spreading technique allows OFDM-based communications systems to exploit frequency diversity. Previous detection algorithms for block-spread OFDM (BSOFDM) are not robust to fast fading, when the communications channel state changes significantly during an OFDM symbol period. It is demonstrated through the use of a new matched filter bound that the performance degradation under fast fading of these algorithms is caused by limitations of the decoding algorithms, and is not an inherent limitation of BSOFDM. An iterative algorithm is introduced with intrinsic data-level parallelism for detecting BSOFDM in the presence of fast fading. detection is first performed independently on sub-blocks of the received symbol vector. Information is exchanged between these parallel detectors in an iterative manner by estimating the interference between the blocks and removing it from the signal vector. It is shown that the computational complexity of this algorithm is not significantly higher than the detection algorithms for BSOFDM over stationary channels, and achieves excellent BER for fast fading with fixed-point arithmetic, making it suitable for use on embedded systems.

关键词： OFDM Fading Iterative algorithms detection algorithms Frequency diversity Robustness Communication channels Matched filters Degradation Iterative decoding

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Linguistic Steganography detection Based on Perplexity

Linguistic Steganography Detection Based on Perplexity

引用

International Conference on MultiMedia and Information Technology, MMIT

作者： Peng Meng Liusheng Huang Zhili Chen Wei Yang Dong Li National High Performance Computing Center Hefei Department of Computer Science and Technology University of Science and Technology Hefei China

Linguistic steganography is a technique for hiding information into text carriers. Most of the previous work on linguistic steganography was focused on steganography and there were few researches on steganalysis. Research on attacking methods against linguistic steganography plays an important role in information security (IS) area. In this paper, a linguistic steganography detection algorithm based on perplexity is presented. An experiment to detect text segments generated by linguistic steganography system NICETEXT is carried out. The result of our experiment is pretty promising. The experimental accuracy of our method on classification of generated text and normal text exceeds 99% when text size is larger than 400 bytes. Even for sentences, the experimental accuracy exceeds 85%.

关键词： Steganography Dictionaries Information security detection algorithms Videos Multimedia computing Information technology High performance computing Computer science Text processing

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A CFAR algorithm for anomaly detection and discrimination in hyperspectral images

A CFAR algorithm for anomaly detection and discrimination in...

引用

IEEE International Conference on Image Processing

作者： Alexis Huck Mireille Guillaume Institut Fresnel Centrale MarseilleCNRSDomaine universitaire de St Jérôme Aix Marseille Université Marseilles France

paper proposes an anomaly detection algorithm for hy- perspectral images. It is unsupervised (the researched spectra are not required a priori), discriminates the anomalies according to their spectra and has a constant false alarm rate (CFAR). The main specificity of this algorithm is to combine these three assets rather than make a tradeoff which is generally necessary with existing methods. It is based on a physically convenient probabilistic model of the fastICA gener ated independent components. We compare it with the adaptive cosine/coherence estimator (a reference supervised target detection algorithm) on a real HYDICE dataset.

关键词： Hyperspectral imaging Object detection detection algorithms Independent component analysis Computer vision Vectors Coherence Image analysis Layout Pixel

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Automatic lattice detection in near-regular histology array images

Automatic lattice detection in near-regular histology array ...

引用

IEEE International Conference on Image Processing

作者： Brian A. Canada Georgia K. Thomas Keith C. Cheng James Z. Wang Yanxi Liu PA USA

Near-regular texture (NRT), denoting deviations from otherwise symmetric wallpaper patterns, is commonly observable in the real world. Existing lattice detection algorithms capture the underlying lattice of an NRT pattern and all of its individual texels, facilitating an automated analysis of NRT. Many real world images, as in those of zebrafish larval histology arrays, depart significantly from regularity and challenge the current state of the art wallpaper group theory-based lattice detection methods. We propose an alternative 2D lattice detection algorithm that exploits translation and reflection symmetries and specific imaging cues. By outperforming existing methods on histology array images, our algorithm leads us towards complete automation of high-throughput histological image processing while broadening the spectrum of NRT computation.

关键词： Lattices detection algorithms Biomedical imaging Microscopy Pattern analysis Algorithm design and analysis Reflection Automation Image processing Biology computing

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Massively Parallel Anomaly detection in Online Network Measurement

Massively Parallel Anomaly Detection in Online Network Measu...

引用

International Conference on Computer Communications and Networks (ICCCN)

作者： Shashank Shanbhag Tilman Wolf Department of Electrical and Computer Engineering University of Massachusetts Amherst MA USA

ISBN: (纸本)9781424423897

Detecting anomalies during the operation of a network is an important aspect of network management and security. Recent development of high-performance embedded processing systems allow traffic monitoring and anomaly detection in real-time. In this paper, we show how such processing capabilities can be used to run several different anomaly detection algorithms in parallel on thousands of different traffic subclasses. The main challenge in this context is to manage and aggregate the vast amount of data generated by these processes. We propose (1) a novel aggregation process that uses continuous anomaly information (rather than binary outputs) from existing algorithms and (2) an anomaly tree representation to illustrate the state of all traffic subclasses. Aggregated anomaly detection results show a lower false positive and false negative rate than any single anomaly detection algorithm.

关键词： detection algorithms Telecommunication traffic Signal processing algorithms Monitoring Prototypes Real time systems Electric variables measurement Predictive models Frequency Floods

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An Outlier detection Algorithm Based on Spectral Clustering

An Outlier Detection Algorithm Based on Spectral Clustering

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Peng Yang Biao Huang Chongqing University of Arts and Sciences Chongqing China

Outlier detection is widely used for many areas such as credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction and marketing. In this paper, we demonstrate the effectiveness of spectral clustering in dataset with outliers. Through spectral method we can use the information of feature space with eigenvectors rather than that of the whole dataset to obtain stable clusters. Then we introduce the cluster-based local outlier factor to identify and find the outliers in dataset. The experimental results show that our outlier detection algorithm outperforms the K-means based algorithm with high precision and low false alarm rate as well as desirable coverage ratio.

关键词： detection algorithms Clustering algorithms Credit cards Electronic commerce Weather forecasting Conferences Computational intelligence Computer industry Electronics industry Art

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